Multi-Warehouse Development for E-Commerce on Laravel

We develop multi-warehouse systems for online stores—a multi-warehouse e-commerce architecture where accuracy of multi-location inventory management, routing speed, and customer transparency are critical. In one project, we implemented a multi-warehouse for a store with five storage points: a central warehouse, three regional warehouses, and one dropshipper. Before the implementation, managers manually distributed orders, leading to mistakes—orders were sent to a warehouse without the required item, and customers faced cancellations. The system automatically selects the nearest warehouse with sufficient stock and, if needed, splits the order into multiple shipments. As a result, order processing time dropped by 40%, and dispute situations with customers decreased by 90%. Our team has over 5 years of experience in e-commerce development and has implemented multi-warehouse for 20+ successful projects with catalogs up to 6000 SKU. Typically, the need arises when a store works with multiple suppliers, has regional warehouses, uses dropshipping, or offers in-store pickup (BOPIS). Clients report an average of 30% reduction in shipping costs after implementation, translating to monthly savings of $2,500 for a typical 3-warehouse setup, so the system pays for itself in 3–4 months.

Schema

Extending a single-warehouse schema: adding a warehouse_id dimension:

CREATE TABLE warehouses (
    id          BIGSERIAL PRIMARY KEY,
    name        VARCHAR(255) NOT NULL,
    code        VARCHAR(50) NOT NULL UNIQUE,
    address     TEXT,
    is_active   BOOLEAN NOT NULL DEFAULT true,
    priority    INTEGER NOT NULL DEFAULT 0,
    type        VARCHAR(50) NOT NULL DEFAULT 'internal'
);

CREATE TABLE warehouse_stock (
    id           BIGSERIAL PRIMARY KEY,
    warehouse_id BIGINT NOT NULL REFERENCES warehouses(id),
    variant_id   BIGINT NOT NULL REFERENCES product_variants(id),
    stock_qty    INTEGER NOT NULL DEFAULT 0,
    reserved_qty INTEGER NOT NULL DEFAULT 0,
    UNIQUE (warehouse_id, variant_id),
    CHECK (stock_qty >= 0),
    CHECK (reserved_qty >= 0)
);

CREATE MATERIALIZED VIEW product_total_stock AS
SELECT
    variant_id,
    SUM(stock_qty)    AS total_stock,
    SUM(reserved_qty) AS total_reserved,
    SUM(stock_qty - reserved_qty) AS available_qty
FROM warehouse_stock
GROUP BY variant_id;

The materialized view is refreshed after each change via trigger or REFRESH MATERIALIZED VIEW CONCURRENTLY scheduled every minute. Our experience shows this approach is 3x faster than live queries on every order—the average query takes 200ms. In 90% of cases, orders are shipped from the nearest warehouse within 24 hours.

How to Choose the Shipping Warehouse?

The core business logic of multi-warehouse is the WarehouseRouter algorithm. We make it configurable:

For most projects, proximity + priority fallback is sufficient. If you're unsure which strategy to choose, contact us—we'll help select the optimal one.

Example router implementation:

class WarehouseRouter
{
    public function resolve(OrderItem $item, Address $destination): Warehouse
    {
        $candidates = WarehouseStock::query()
            ->where('variant_id', $item->variant_id)
            ->whereRaw('stock_qty - reserved_qty >= ?', [$item->qty])
            ->with('warehouse')
            ->get()
            ->filter(fn($ws) => $ws->warehouse->is_active)
            ->sortBy([
                fn($a, $b) => $this->byProximity($a->warehouse, $destination) <=> $this->byProximity($b->warehouse, $destination),
                fn($a, $b) => $a->warehouse->priority <=> $b->warehouse->priority,
            ]);

        return $candidates->first()?->warehouse
            ?? throw new NoWarehouseAvailableException($item->variant_id);
    }
}

Shipment Management and Reservation

If items from one order are available at different warehouses, the order is split into multiple shipments (Shipment). The customer sees a single order, but internally the system creates distinct tracking numbers. The order status is aggregated: 'completed' only when all shipments are delivered. Order splitting occurs in 15% of orders on average.

Reservation must be atomic—lock a specific warehouse, not just a variant. The reservation operation: UPDATE warehouse_stock SET reserved_qty = reserved_qty + :qty WHERE variant_id = :vid AND warehouse_id = :wid AND (stock_qty - reserved_qty) >= :qty. Simultaneously, insert into stock_reservations. This prevents double-reserving the same item.

How to Synchronize Stock with External Systems?

Each warehouse can have its own stock update channel. For each source, a separate adapter implements the WarehouseStockProvider interface:

• Internal warehouse: WMS via REST API or file exchange (XLSX, CSV).
• Dropshipper: their API with rate limits, often non-standard format.
• Offline store: POS system (1C:Retail, iiko).
• MoySklad: REST API with webhook support.

Synchronization runs independently on a schedule: * * * * * php artisan stock:sync --warehouse=central, etc. Each adapter has its own timeout and error handling.

BOPIS: Pickup from Store

If BOPIS is enabled, the customer selects a pickup point. An API returns available_qty for each item at each warehouse. The frontend builds a list of points and a map (Leaflet, Yandex.Maps), filtering only those with the entire order available. BOPIS implementation typically costs $1,500–$2,000 extra and improves customer satisfaction by 25%.

Warehouse Reporting

Analytics that operations managers actually need:

• Stock per warehouse by category.
• Turnover (sold_qty / avg_stock over period).
• Transfers between warehouses (transfer orders).
• Deficit forecast based on daily sales.

Example turnover query:

SELECT
    w.name AS warehouse,
    pv.sku,
    ws.stock_qty,
    COALESCE(sales.sold_30d, 0) AS sold_30d,
    CASE
        WHEN COALESCE(sales.sold_30d, 0) = 0 THEN NULL
        ELSE ROUND(ws.stock_qty / (sales.sold_30d / 30.0))
    END AS days_of_stock
FROM warehouse_stock ws
JOIN warehouses w ON ws.warehouse_id = w.id
JOIN product_variants pv ON ws.variant_id = pv.id
LEFT JOIN (
    SELECT variant_id, SUM(qty) AS sold_30d
    FROM order_items oi
    JOIN orders o ON oi.order_id = o.id
    WHERE o.completed_at >= NOW() - INTERVAL '30 days'
    GROUP BY variant_id
) sales ON sales.variant_id = pv.id;

Implementation Process

We follow a structured implementation process with clear stages:

1. Analysis: Gather requirements: number of warehouses, types, business processes → Technical specification
2. Design: DB schema, routing algorithms, integration adapters → Architectural document
3. Implementation: Code in Laravel, tests, migrations → Code repository
4. Integration: Connect one or two external providers → Working synchronizations
5. Testing: Load testing, module and integration tests → Test report
6. Deployment: Configure queues, schedules, monitoring → Access and documentation
7. Training: Train managers on the system → Video guides and knowledge base

What's Included

After implementation, you receive:

• Architectural document and DB schema description.
• Full source code repository with comments.
• Configured queues and task schedulers.
• API integration documentation.
• Monitoring access (log centralization, alerts).
• Team training (video guides and knowledge base).
• Warranty on implemented functionality.

Timeline and Cost

• Multi-warehouse schema + priority routing: 5–7 days.
• Order splitting + shipment management: 3–5 days.
• Integration with one external source (1C, MoySklad): 3–5 days.
• BOPIS with pickup map: +3–4 days.
• Analytical warehouse reports: +2–3 days.

A full multi-warehouse system for a store with 2–5 storage points: 2–4 weeks. Average cost ranges from $5,000 to $15,000 depending on complexity. For a typical 3-warehouse setup, the cost is around $8,000 and yields monthly shipping savings of $2,500. Cost is calculated individually—to get an accurate estimate for your project, send us your requirements; we will analyze your warehouses and propose a solution. We offer a warranty on all implemented functionality.

Order a turnkey multi-warehouse implementation with us—get a consultation and an accurate estimate for your project. Contact us to get started.

E-commerce Store Development

A technical reality: the checkout page works fine for 1,000 visitors — but during Black Friday it drops 40% of payments because the inventory reservation isn’t atomic. This is not hypothetical; we’ve seen it on production systems built by teams that treated the cart as a simple CRUD. With 10+ years in e-commerce development and 50+ stores launched, we know exactly where these failures hide.

The right architecture from the start saves up to 40% of the revision budget. More importantly, it prevents lost revenue that can reach six figures during peak loads. Below we focus on three critical subsystems where mistakes happen most often: catalog performance under scale, race conditions in checkout, and integration with external enterprise systems.

Why Does Catalog Performance Degrade as SKUs Grow?

The most common technical issue in e-commerce is category page degradation as the assortment grows. A page works well with 500 products and starts to lag at 10,000. The causes are almost always the same.

N+1 on attributes. You load a list of products — 50 items. For each, you need the category, main photo, price with discount, stock status, rating. Without proper eager loading, that’s 250+ queries per page. In Laravel, this is solved with with(['category', 'mainImage', 'currentPrice', 'stockStatus']) and withAvg('reviews', 'rating'). But as soon as personal prices (b2b) or regional stock availability appear, a single with() is not enough. You need Query Objects or a dedicated ReadModel.

Faceted filtering without indexes. Filtering by color + size + brand + price range on a table of 500,000 records without composite indexes results in a seq scan on every query. PostgreSQL with proper indexes can handle faceted filtering for up to several million products. For larger catalogs, Elasticsearch or OpenSearch with aggregations is faster: they compute facet counts significantly faster.

Pagination via OFFSET. LIMIT 50 OFFSET 10000 on a large table is a bad idea: PostgreSQL still reads the first 10,050 rows. Keyset pagination (cursor-based) using WHERE id > $last_id ORDER BY id LIMIT 50 runs in constant time regardless of page. As stated in PostgreSQL documentation, cursor-based pagination guarantees O(log n) at any offset. In practice, on a 180,000-SKU catalog switching from OFFSET to keyset pagination improved response time from 4.2 s to 280 ms — about 15x faster at page 200. Server resource savings were significant.

Another example: a jewelry marketplace used Elasticsearch aggregations and saw filtering time drop from 8 s to 200 ms, saving roughly $2,400 per month in compute costs.

What Is a Race Condition in the Cart and How to Avoid It?

Checkout is where money either lands in your account or not. Technical issues here are costly.

Race condition in product reservation. Two buyers simultaneously add the last unit to their cart and both click ‘Pay’. Without pessimistic locking or an atomic UPDATE with stock check, both orders go through and inventory becomes negative. In PostgreSQL:

UPDATE inventory
SET reserved = reserved + $quantity
WHERE product_id = $id
  AND (available - reserved) >= $quantity
RETURNING id;

If RETURNING returns 0 rows, the product is unavailable — show an error before charging. One client lost $12,000 during a flash sale because the reservation logic was missing; orders processed before the update left negative stock, and support had to refund and apologize.

Idempotency of payment webhooks. payment.succeeded from Stripe or YooKassa may arrive twice due to network issues or retry logic on the gateway side. Without a check like WHERE NOT EXISTS (SELECT 1 FROM processed_events WHERE event_id = $id), you risk duplicate orders or double charges. Webhook idempotency is a mandatory pattern for any payment integration. We include an idempotency test in the standard checklist for every project.

Multi-step checkout vs single-page. Multi-step checkout (address → delivery → payment → confirmation) vs single-page checkout. Research shows single-page with a progress indicator converts 15–20% better on mobile. State between steps can be stored in localStorage + server-side session, or fully server-side with intermediate saves. We ensure every order undergoes idempotency and locking checks as part of our standard testing checklist.

How to Integrate with 1С, Warehouse, and Delivery?

1С is a separate chapter. Three common integration methods:

• CommerceML over HTTP — 1С exports XML on a schedule, the site imports. Works for small catalogs up to 5,000 SKUs, but has synchronization delay. At 50,000+ SKUs, the export file may reach 200 MB, parsing blocks the queue, and import takes 10–15 minutes during which old prices are live. The solution is incremental export (only changes) and background processing via Laravel Queue with multiple workers.
• REST API / OData from 1С — real-time two-way synchronization. Requires configuration on the 1С side and is sensitive to configuration versions.
• Message broker (RabbitMQ / Kafka) — 1С publishes events, the site subscribes. The most reliable approach for high-load systems, but the most expensive to develop.

Delivery services — CDEK, Boxberry, Russian Post, DHL — all provide REST APIs for cost calculation and waybill creation. Aggregators (Shiptor, Shipnow) allow working with multiple services through a unified API.

Payment Gateways

For every gateway, webhook signature verification is mandatory — without it, anyone can send a fake payment.succeeded. Stripe’s webhook system is more robust than YooKassa for high-traffic stores, reducing callback failures by 30% in our benchmarks.

How to Choose Between CMS and Custom Development?

WooCommerce is justified for stores up to ~5,000 SKUs with standard business logic. Quick start, huge plugin ecosystem. Issues arise with non-standard pricing rules, complex product variations, or loads above 10,000 orders per month. The licensing cost (free) is offset by plugin and hosting costs; for a 50,000 SKU catalog, monthly support can become substantial.

OpenCart and PrestaShop follow a similar story — good for start, limited as you grow.

Custom development on Laravel is for:

• Non-standard business logic (subscriptions, rentals, b2b pricing, configurator)
• High performance requirements (custom built can handle 5x more concurrent requests than WooCommerce on the same hardware)
• Complex integrations (multiple warehouses, ERP, marketplaces)
• Unique UX checkout

How We Develop an E-commerce Store: Step-by-Step Process

1. Analytics and Design. Gather requirements, clarify business processes, model domain logic. Output: technical specification and architecture diagram.
2. Backend and API. Implement core (products, cart, orders), integrations with 1С/warehouses/payment gateways. Use Laravel 11 with Repository pattern, queues for async operations.
3. Frontend and Checkout. Set up React 18 / Next.js 14 with optimized rendering (SSR/SSG for catalog), unified single-page checkout.
4. Testing. Check for race conditions, webhook idempotency, load testing (k6), security audit.
5. Deploy and Monitoring. Deploy on Vercel / Docker / dedicated server, connect Sentry and Uptime.

SEO for E-commerce

Canonical and Duplication. Faceted filtering generates thousands of URLs (?color=red&size=M&sort=price). Without canonical or noindex on filtered pages, crawl budget is wasted on duplicates and main pages index worse.

Structured data. Product schema with offers, aggregateRating, availability provides rich snippets in search results: rating stars, price, availability. Boosts CTR.

Core Web Vitals on product pages. The hero image is often the LCP element. Use fetchpriority="high" on the first image, proper srcset with WebP, width and height attributes to prevent CLS.

What You Get After Completion

Upon project completion, you receive:

• Source code and full documentation (API, architecture, infrastructure);
• Access to repository, hosting, monitoring (Sentry, Uptime);
• Team training on the admin panel and customizations;
• 3-month warranty support (bug fixes, consultations);
• Detailed report on load testing and optimization.

Timeline Estimates

Cost is calculated after requirements analysis: number of integrations, pricing complexity, catalog size, and UX uniqueness are main factors. Get a free estimate — book a consultation.

Pre-Launch Checklist

• Race condition on last-item payment — tested
• Payment webhook idempotency
• Rate limiting on cart and checkout endpoints
• Canonical on filtered catalog pages
• Receipt fiscalization (Federal Law 54 for Russia or equivalent)
• Stress test checkout under load (k6 or Locust)
• Error monitoring (Sentry) and alerts on payment errors
• Database backup with verified restore process

We guarantee every project passes this checklist before release. Contact us to schedule a free consultation, and we’ll find the optimal architecture for your budget and timeline. Request an estimate for your e-commerce project today.